Creating a dashboard to understand the impact of marketing on sales
Problem: The client had 9 different product categories across 2 different brands that were being sold in 7 different retailers and advertised on multiple traditional and digital channels. The client’s leadership team was struggling to create a report that visualized sales and marketing performance to understand the impact of marketing.
Solution: We created a dashboard that updated daily with sales and marketing data. The dashboard featured a line graph that showed sales and ad spend over time and could be filtered by brand and product category, giving leadership an instant snapshot into marketing performance for the brand and product category of their choosing. The dashboard additionally included drill-down maps that showed sales at the state, county, and zip code levels and graphs showing the top selling products within a category and overall. These more granular visualizations helped leadership to make informed decisions regarding where and what to market.
Identifying members vs. non-members in GA4
Problem: The client wanted to distinguish between users that logged into their website (i.e. members) versus users who didn’t log in (i.e. potential members) within Google Analytics.
Solution: One Analytics set up tracking in Google Tag Manager and GA4 to enable the “Signed in with user ID” dimension in GA4, enabling the client to see members versus potential members.
Analyzing the competition
The Client’s Question: How much competition does each of our 16 branches face? What percent of the market has each branch already saturated?
Answer: Using lists of the client’s key competitors, the client’s customers, and population data from the U.S. Census Bureau, we identified the level of competition and the level of market saturation for each branch. We recommended that the client stop spending marketing dollars on branches where they had already heavily saturated the market and reallocate those dollars towards branches that had low saturation and low competition. For branches facing high competition, we recommended focusing marketing’s message on what distinguished our client from the competition. As a result, the client deployed a more targeted and efficient marketing strategy.
Identifying new vs. returning customers
Problem: The client switched to Shopify from another system. Each time a customer purchased an item from the client’s new Shopify store, Shopify would flag the customer as new even if they had made purchases from the client before the switch. The client needed an automated solution to determine if a customer was truly new or returning. Without this information, the client couldn’t fully understand the return on investment for their marketing campaigns.
Solution: Using Extract, Transform, Load (ETL), we combined data from Shopify and the client’s previous system. We used the ETL to look up customers using a unique identifier and to categorize them as new vs. returning daily when new Shopify data came in. We created visualizations to showcase the number of new and returning customers to give the client a complete and accurate picture of who was purchasing. As a result, the client was able to evaluate and optimize their marketing campaigns.
